Network operator identification through RF fingerprinting of base stations

In this work, we aim to identify network operators through radio frequency fingerprinting (RFF) of base stations (BSs). We propose to do so based on the classification of synchronization signals, using a complex-valued convolutional neural network (CNN) architecture. Our results indicate that LTE operators can be identified from raw in-phase and quadrature (I/Q) samples collected over a live network with an accuracy of more than 96%.